from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
%matplotlib notebook

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 20s

KMeans_tall: 0h 0m 23s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 28s

KNeighborsClassifier_kd_tree: 0h 2m 37s

daal4py_KNeighborsClassifier: 0h 2m 47s

lightgbm: 0h 5m 1s

HistGradientBoostingClassifier: 0h 5m 1s

catboost_symmetric: 0h 5m 2s

xgboost: 0h 5m 4s

catboost_lossguide: 0h 5m 7s

KNeighborsClassifier: 0h 33m 38s

total: 1h 6m 4s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.5",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.154	0.000	5.195	0.000	1	5	NaN	NaN	0.517	0.000	0.298	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.999	0.165	0.000	0.023	1	5	0.788	0.939	2.103	0.013	10.935	0.102	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.219	0.001	0.000	0.219	1	5	1.000	1.000	0.086	0.002	2.532	0.075	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.127	0.000	6.306	0.000	1	1	NaN	NaN	0.507	0.000	0.250	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.749	0.062	0.000	0.013	1	1	0.690	0.806	1.983	0.026	6.429	0.089	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.205	0.003	0.000	0.205	1	1	0.000	1.000	0.084	0.001	2.431	0.045	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.000	6.333	0.000	-1	100	NaN	NaN	0.478	0.000	0.264	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	34.480	0.000	0.000	0.034	-1	100	0.939	0.806	1.936	0.020	17.808	0.185	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.205	0.020	0.000	0.205	-1	100	0.000	1.000	0.083	0.000	2.487	0.239	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.220	0.000	-1	5	NaN	NaN	0.468	0.000	0.275	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	34.383	0.000	0.000	0.034	-1	5	0.788	0.699	1.926	0.029	17.852	0.269	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.207	0.019	0.000	0.207	-1	5	1.000	1.000	0.084	0.001	2.450	0.224	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.127	0.000	6.315	0.000	1	100	NaN	NaN	0.480	0.000	0.264	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.033	0.143	0.000	0.023	1	100	0.939	0.699	1.987	0.025	11.592	0.164	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.219	0.002	0.000	0.219	1	100	0.000	1.000	0.086	0.002	2.533	0.062	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.000	6.429	0.000	-1	1	NaN	NaN	0.481	0.000	0.259	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.802	0.241	0.000	0.024	-1	1	0.690	0.939	2.070	0.051	11.499	0.305	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.018	0.000	0.183	-1	1	0.000	1.000	0.087	0.003	2.110	0.228	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.264	0.000	1	5	NaN	NaN	0.110	0.000	0.551	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.029	0.181	0.000	0.021	1	5	0.987	0.981	0.364	0.005	57.846	0.943	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.000	0.026	1	5	1.000	1.000	0.006	0.000	4.120	0.317	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.281	0.000	1	1	NaN	NaN	0.111	0.000	0.513	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.870	0.045	0.000	0.010	1	1	0.969	0.979	0.308	0.006	32.076	0.610	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.000	0.000	0.015	1	1	1.000	1.000	0.006	0.001	2.416	0.358	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.261	0.000	-1	100	NaN	NaN	0.108	0.000	0.566	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.978	0.000	0.000	0.032	-1	100	0.986	0.979	0.300	0.005	106.599	1.765	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.034	0.002	0.000	0.034	-1	100	1.000	1.000	0.006	0.001	5.759	0.680	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.066	0.000	0.243	0.000	-1	5	NaN	NaN	0.107	0.000	0.613	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.127	0.000	0.000	0.032	-1	5	0.987	0.971	0.307	0.009	104.684	3.080	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.034	0.002	0.000	0.034	-1	5	1.000	1.000	0.006	0.000	5.942	0.475	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.000	0.273	0.000	1	100	NaN	NaN	0.113	0.000	0.517	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.824	0.076	0.000	0.021	1	100	0.986	0.971	0.306	0.006	68.044	1.402	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.027	0.001	0.000	0.027	1	100	1.000	1.000	0.006	0.000	4.629	0.414	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.269	0.000	-1	1	NaN	NaN	0.107	0.000	0.556	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.203	0.169	0.000	0.021	-1	1	0.969	0.981	0.364	0.020	58.288	3.302	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.003	0.000	0.023	-1	1	1.000	1.000	0.006	0.001	3.798	0.936	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.906	0.000	0.028	0.000	-1	1	NaN	NaN	0.749	0.000	3.877	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.448	0.008	0.000	0.000	-1	1	0.961	0.956	0.101	0.001	4.432	0.103	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	14.766	7.398	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.977	0.000	0.027	0.000	1	5	NaN	NaN	0.736	0.000	4.044	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.369	0.013	0.000	0.001	1	5	0.971	0.971	0.550	0.009	2.491	0.047	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.056	0.932	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.916	0.000	0.027	0.000	1	100	NaN	NaN	0.743	0.000	3.924	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.591	0.061	0.000	0.005	1	100	0.968	0.974	0.183	0.005	25.119	0.804	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	1	100	1.000	1.000	0.000	0.000	16.013	7.965	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.988	0.000	0.027	0.000	1	1	NaN	NaN	0.712	0.000	4.194	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.717	0.012	0.000	0.001	1	1	0.961	0.971	0.544	0.014	1.319	0.041	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	1.834	0.705	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.949	0.000	0.027	0.000	-1	5	NaN	NaN	0.710	0.000	4.156	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.837	0.011	0.000	0.001	-1	5	0.971	0.974	0.187	0.011	4.470	0.259	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.000	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	11.837	5.315	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.089	0.000	0.026	0.000	-1	100	NaN	NaN	0.767	0.000	4.025	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.803	0.039	0.000	0.003	-1	100	0.968	0.956	0.100	0.002	28.009	0.775	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.008	0.002	0.000	0.008	-1	100	1.000	1.000	0.000	0.000	34.520	18.065	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.856	0.000	0.019	0.000	-1	1	NaN	NaN	0.527	0.000	1.625	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	1	0.982	0.973	0.001	0.000	27.552	12.738	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.757	13.422	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.859	0.000	0.019	0.000	1	5	NaN	NaN	0.494	0.000	1.739	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	1	5	0.985	0.981	0.007	0.002	3.234	0.776	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.926	2.954	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.809	0.000	0.020	0.000	1	100	NaN	NaN	0.482	0.000	1.679	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.050	0.002	0.000	0.000	1	100	0.985	0.980	0.002	0.001	32.420	10.635	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.384	3.823	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.810	0.000	0.020	0.000	1	1	NaN	NaN	0.473	0.000	1.712	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.022	0.001	0.001	0.000	1	1	0.982	0.981	0.008	0.001	2.891	0.345	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.939	3.204	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.812	0.000	0.020	0.000	-1	5	NaN	NaN	0.504	0.000	1.610	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.028	0.001	0.001	0.000	-1	5	0.985	0.980	0.002	0.002	13.240	11.240	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.001	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	20.834	14.041	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.864	0.000	0.019	0.000	-1	100	NaN	NaN	0.470	0.000	1.836	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.047	0.001	0.000	0.000	-1	100	0.985	0.973	0.001	0.000	48.940	17.801	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.884	15.259	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.663	0.0	0.724	0.000	random	NaN	30	NaN	0.480	0.0	1.382	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.338	0.000	random	0.001	30	0.001	0.000	0.0	9.288	5.845	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.920	7.182	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.616	0.0	0.780	0.000	k-means++	NaN	30	NaN	0.411	0.0	1.497	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.339	0.000	k-means++	0.001	30	0.001	0.000	0.0	9.277	5.528	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.538	6.769	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.705	0.0	3.580	0.000	random	NaN	30	NaN	2.937	0.0	2.283	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	12.398	0.000	random	0.002	30	0.002	0.000	0.0	6.555	3.036	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.017	0.001	random	1.000	30	1.000	0.000	0.0	9.911	6.260	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.439	0.0	3.727	0.000	k-means++	NaN	30	NaN	2.803	0.0	2.297	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.823	0.000	k-means++	0.001	30	0.002	0.000	0.0	5.976	2.635	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.017	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.158	5.086	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.087	0.000	0.037	0.000	random	NaN	20	NaN	0.103	0.0	0.843	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.163	0.000	random	0.000	20	0.002	0.001	0.0	2.782	0.579	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	8.958	5.268	See	See
3	KMeans_short	fit	10000	10000	2	20	0.238	0.000	0.013	0.000	k-means++	NaN	20	NaN	0.039	0.0	6.110	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.167	0.000	k-means++	-0.000	20	0.004	0.001	0.0	2.461	0.438	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.180	5.101	See	See
6	KMeans_short	fit	10000	10000	100	20	0.217	0.000	0.737	0.000	random	NaN	20	NaN	0.377	0.0	0.577	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.000	6.005	0.000	random	0.307	20	0.311	0.001	0.0	1.781	0.444	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.011	0.002	random	1.000	20	1.000	0.000	0.0	7.596	4.143	See	See
9	KMeans_short	fit	10000	10000	100	20	0.660	0.000	0.242	0.000	k-means++	NaN	20	NaN	0.148	0.0	4.450	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.001	4.923	0.000	k-means++	0.350	20	0.323	0.001	0.0	2.338	0.654	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.933	5.711	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.902	0.0	[-0.09913314]	0.000	NaN	NaN	NaN	NaN	NaN	1.931	0.0	6.163	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.15511454]	0.000	NaN	NaN	NaN	NaN	0.549	0.000	0.0	0.853	0.422	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.20150651]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.426	0.350	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.778	0.0	[-2.74375519]	0.001	NaN	NaN	NaN	NaN	NaN	0.711	0.0	1.095	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[118.75225032]	0.000	NaN	NaN	NaN	NaN	0.160	0.003	0.0	0.583	0.125	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[20.4715081]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.108	0.078	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.187	0.000	0.428	0.0	NaN	NaN	NaN	0.191	0.000	0.976	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.001	8.015	0.0	NaN	NaN	0.105	0.017	0.001	0.600	0.043	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.871	0.0	NaN	NaN	NaN	0.000	0.000	0.741	0.700	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.546	0.000	0.518	0.0	NaN	NaN	NaN	0.260	0.000	5.939	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	5.185	0.0	NaN	NaN	1.000	0.000	0.000	0.685	0.451	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.012	0.0	NaN	NaN	NaN	0.000	0.000	0.660	0.610	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw fit times

Raw predict times

Smoothed HPO Curves

The shaded areas represent boostrapped quartiles.

Speedup barplots

Speedup curves